Predict new compounds_LCvsGC.ipynb

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  "metadata": {
    "colab": {
      "name": "Predict LC GC.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "_Qf4IiaZjj4P",
        "V7Ut2zMyjqdZ"
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Load packages"
      ],
      "metadata": {
        "id": "_Qf4IiaZjj4P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pz0pGe_dajg2",
        "outputId": "7aa372c8-9015-4ee4-b7ec-7d8801a725d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install -r http://users.uoa.gr/~nalygizakis/Applied%20Data%20Science/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from time import time\n",
        "from collections import Counter\n",
        "\n",
        "import graphviz\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "\n",
        "from joblib import dump, load\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, MultiLabelBinarizer\n",
        "from sklearn.feature_selection import SelectFromModel, VarianceThreshold, RFECV, SequentialFeatureSelector\n",
        "from sklearn.model_selection import GridSearchCV, KFold, RepeatedKFold, cross_validate, train_test_split, validation_curve\n",
        "from sklearn.metrics import multilabel_confusion_matrix, classification_report, roc_curve, auc, accuracy_score, hamming_loss, make_scorer\n",
        "\n",
        "import pingouin as pg\n",
        "import scikit_posthocs as sp\n",
        "from mlxtend.evaluate import mcnemar\n",
        "from mlxtend.evaluate import mcnemar_table\n",
        "\n",
        "sns.set()\n",
        "import warnings\n",
        "warnings.simplefilter(\"ignore\", UserWarning)\n",
        "warnings.simplefilter(\"ignore\", FutureWarning)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKB48FzdayW8",
        "outputId": "1979b676-726f-4306-89e2-160112e8692c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load some helper functions"
      ],
      "metadata": {
        "id": "V7Ut2zMyjqdZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def qconstant_filtering(X, thresh=0.01):\n",
        "  '''Remove constant and quasi-constant features.'''\n",
        "\n",
        "  qconstant_filter = VarianceThreshold()\n",
        "  qconstant_filter.fit(X)\n",
        "\n",
        "  qfeatures = X.iloc[:, qconstant_filter.variances_ <= thresh].columns\n",
        "\n",
        "  print(f\"Quasi-Constant features found: {len(qfeatures)}\")\n",
        "\n",
        "  X = X.drop(labels=qfeatures, axis=1)\n",
        "\n",
        "  print(f\"New feature shape: {X.shape}\")\n",
        "  return X\n",
        "\n",
        "\n",
        "def correlation_filtering(X, thresh=0.9, method='spearman'):\n",
        "    '''Remove correlated features.'''\n",
        "\n",
        "    # Set of all the names of deleted columns\n",
        "    col_corr = set()\n",
        "\n",
        "    # Create correlation matrix from the dataset\n",
        "    corr_matrix = X.corr(method=method).abs()\n",
        "\n",
        "    for i in range(len(corr_matrix.columns)):\n",
        "        for j in range(i):\n",
        "            if (corr_matrix.iloc[i, j] >= thresh) and (corr_matrix.columns[j] not in col_corr):\n",
        "                colname = corr_matrix.columns[i] # getting the name of column\n",
        "                col_corr.add(colname)\n",
        "                if colname in X.columns:\n",
        "                    del X[colname] # deleting the column from the dataset\n",
        "\n",
        "    print(f\"Correlated features found: {len(col_corr)}\")\n",
        "    print(f\"New feature shape: {X.shape}\")\n",
        "    return X\n",
        "\n",
        "def feature_importance(model, X, Y, thresh='1.5*mean'):\n",
        "    '''Select features based on importance weights.'''\n",
        "\n",
        "    # Initialize and apply transformer\n",
        "    forest_sel = SelectFromModel(model, threshold=thresh)\n",
        "\n",
        "    tic = time()\n",
        "\n",
        "    forest_sel.fit(X, Y)\n",
        "\n",
        "    toc = time()\n",
        "\n",
        "    # Transform X feature set\n",
        "    X = X.iloc[:, forest_sel.get_support()]\n",
        "\n",
        "    print(f\"Done in {(toc - tic)/60:0.2f} minutes\")\n",
        "    print(f\"New feature shape: {X.shape}\")\n",
        "    return X\n",
        "\n",
        "def rfecv_selection(model, X, Y, cv=10, scoring=None, figure=False):\n",
        "    '''Feature ranking with recursive feature elimination and\n",
        "    cross-validated selection of the best number of features.'''\n",
        "\n",
        "    # Initialize and apply RFECV\n",
        "    rfecv = RFECV(estimator=model, step=1, cv=cv, scoring=scoring, n_jobs=-1)\n",
        "\n",
        "    tic = time()\n",
        "\n",
        "    rfecv.fit(X, Y)\n",
        "\n",
        "    toc = time()\n",
        "\n",
        "    # Transform X feature set\n",
        "    X = X.iloc[:, rfecv.support_]\n",
        "\n",
        "    print(f\"Done in {(toc - tic)/60:0.2f} minutes\")\n",
        "    print(f\"Optimal number of features: {rfecv.n_features_}\")\n",
        "    print(f\"New feature shape: {X.shape}\")\n",
        "\n",
        "    # Plot RFECV search\n",
        "    if figure == True:\n",
        "\n",
        "        # Plot number of features VS. cross-validation scores\n",
        "        plt.figure(figsize=(16,9))\n",
        "\n",
        "\n",
        "        plt.xticks(np.arange(0, 56, 5).astype(int))\n",
        "        plt.xlabel(\"Number of features selected\", fontsize=15)\n",
        "        plt.ylabel(\"Cross validation score (Accuracy)\", fontsize=15)\n",
        "        plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
        "\n",
        "        plt.savefig('RFECV.png')\n",
        "        plt.show()\n",
        "\n",
        "    return X\n",
        "\n",
        "def forward_selection(model, X, Y, cv=10, n_feat=10, scoring=None, feat_only=False):\n",
        "    '''Perform Sequential Feature Selection in the forward direction.'''\n",
        "\n",
        "    # Initialize and apply Sequential Forward selection\n",
        "    sfs_forward = SequentialFeatureSelector(model, n_features_to_select=n_feat, cv=cv,\n",
        "                                            direction='forward', scoring=scoring, n_jobs=-1)\n",
        "\n",
        "    tic = time()\n",
        "\n",
        "    sfs_forward.fit(X, Y)\n",
        "\n",
        "    toc = time()\n",
        "\n",
        "    # Transform X and get feature set\n",
        "    X = X.iloc[:, sfs_forward.get_support()]\n",
        "    features = X.columns.to_list()\n",
        "\n",
        "    print(f\"Done in {(toc - tic)/60:0.2f} minutes\")\n",
        "\n",
        "    if feat_only == True:\n",
        "        return features\n",
        "\n",
        "    print(f\"New feature shape: {X.shape}\")\n",
        "    print(f\"Features selected by sequential forward selection: {features}\")\n",
        "    return X\n",
        "\n",
        "def manual_selection(features, thresh=0.5):\n",
        "    '''Manually select robust features by calculating overlap.'''\n",
        "\n",
        "    # Calculate feature overlap\n",
        "    count_overlap = Counter(x for xs in features for x in set(xs))\n",
        "\n",
        "    # Initialize list to collect features\n",
        "    sel_feat = []\n",
        "    start = len(features)\n",
        "    stop = int(len(features)*thresh)\n",
        "\n",
        "\n",
        "    for i in range(start, stop, -1):\n",
        "        for k, v in count_overlap.items():\n",
        "            if v == i and len(sel_feat) < 10:\n",
        "                sel_feat.append(k)\n",
        "\n",
        "    print(f'Selected features with good overlap: {sel_feat}')\n",
        "\n",
        "    return sel_feat"
      ],
      "metadata": {
        "id": "Zq5uCt3sjv3I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def hamming_score(y_true, y_pred, normalize=True, sample_weight=None):\n",
        "    '''Compute the Hamming score (i.e., label-based accuracy) for the multi-label case.'''\n",
        "\n",
        "    acc_list = []\n",
        "    for i in range(y_true.shape[0]):\n",
        "        set_true = set( np.where(y_true[i])[0] )\n",
        "        set_pred = set( np.where(y_pred[i])[0] )\n",
        "        tmp_a = None\n",
        "        if len(set_true) == 0 and len(set_pred) == 0:\n",
        "            tmp_a = 1\n",
        "        else:\n",
        "            tmp_a = len(set_true.intersection(set_pred))/\\\n",
        "                    float( len(set_true.union(set_pred)) )\n",
        "        acc_list.append(tmp_a)\n",
        "    return np.mean(acc_list)\n",
        "\n",
        "def model_comparison(models, X, Y, cv=10, scoring=None):\n",
        "    '''Model comparison using dictionary with different pipelines.'''\n",
        "\n",
        "    # Initialize lists to store results\n",
        "    names = []\n",
        "    results = []\n",
        "\n",
        "    # k-fold CV results\n",
        "    for key, value in models.items():\n",
        "        cv_results = cross_validate(value['pl'], X, Y, cv=cv, scoring = scoring, n_jobs=4)\n",
        "        results.append(cv_results)\n",
        "        names.append(value['name'])\n",
        "\n",
        "        if scoring:\n",
        "            print(f\"\\n{value['name']:<11}: Exact Match Ratio =    {cv_results['test_Exact Match Ratio'].mean():0.3f} ({cv_results['test_Exact Match Ratio'].std():0.3f})\")\n",
        "            print(f\"{' ':<11}  Label-based Accuracy = {cv_results['test_Label-based Accuracy'].mean():0.3f} ({cv_results['test_Label-based Accuracy'].std():0.3f})\")\n",
        "        else:\n",
        "            print(f\"{value['name']:<11}: {cv_results['test_score'].mean():0.3f} ({cv_results['test_score'].std():0.3f})\")\n",
        "\n",
        "    return results, names\n",
        "\n",
        "def feature_comparison(model, X, Y, cv=10, scoring=None, X_names=None, save=True):\n",
        "    '''Perform cross-validation using the same classifier with different feature sets.'''\n",
        "\n",
        "    # Initialize dataframe to store results\n",
        "    ds_result = pd.DataFrame()\n",
        "\n",
        "    # Perform cross-validation and save the results\n",
        "    if X_names:\n",
        "        for name, data in zip(X_names, X):\n",
        "            cv_results = cross_validate(model, data, Y, cv=cv, scoring=scoring, n_jobs=4)\n",
        "            ds_result[name] = cv_results['test_score']\n",
        "    else:\n",
        "        for idx, data in enumerate(X):\n",
        "            cv_results = cross_validate(model, data, Y, cv=cv, scoring=scoring, n_jobs=4)\n",
        "            ds_result[idx] = cv_results['test_score']\n",
        "\n",
        "    if save == True:\n",
        "        ds_result.to_csv('Performance of different feature sets in CV.csv', index=None)\n",
        "\n",
        "    return ds_result\n",
        "\n",
        "def accuracy_per_class(y_true, y_pred):\n",
        "    '''Calculate accuracy per class.'''\n",
        "\n",
        "    # Compute confusion matrix for the multilabel case\n",
        "    cm = multilabel_confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    # Calculate accuracy for each label (TP + TN)/(TP + FP + TN + FN)\n",
        "    gc_acc = cm[0].diagonal().sum()/cm[0].sum()\n",
        "    lc_acc = cm[1].diagonal().sum()/cm[1].sum()\n",
        "\n",
        "    return (gc_acc, lc_acc)\n",
        "\n",
        "def general_classification_report(y_true, y_pred, title='Classification Report'):\n",
        "    '''Show a Classification Report with accuracy.'''\n",
        "\n",
        "    print(title)\n",
        "    print('-'*len(title))\n",
        "    print(classification_report(y_true, y_pred, zero_division=0))\n",
        "\n",
        "    print(\"\\nAccuracy per class\")\n",
        "    print(f'GC = {accuracy_per_class(y_true, y_pred)[0]:0.3f}')\n",
        "    print(f'LC = {accuracy_per_class(y_true, y_pred)[1]:0.3f}')\n",
        "\n",
        "    print(f'\\nLabel-based accuracy = {hamming_score(y_true, y_pred):0.3f}')\n",
        "\n",
        "    return classification_report(y_true, y_pred, zero_division=0, output_dict=True)\n",
        "\n",
        "def show_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "    '''Calculate and plot confusion matrix.'''\n",
        "\n",
        "    # Plot heatmap from confusion matrix\n",
        "    ds_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(ds_cm, annot=True, fmt=\"d\", cbar=False, ax=axes, cmap='Blues')\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Confusion Matrix for the \" + class_label + \" class\", fontsize=14)\n",
        "\n",
        "def plot_roc_curve(y_true, y_pred1, y_pred2, model_names, class_names=None, title='ROC curve', save=False):\n",
        "    '''Calculate and plot ROC curve.'''\n",
        "\n",
        "    # Get number of classes/labels\n",
        "    n_classes = y_true.shape[1]\n",
        "\n",
        "    # Compute ROC curve and ROC area for each class\n",
        "    fpr, tpr, roc_auc = dict(), dict(), dict()\n",
        "\n",
        "    for i in range(0, n_classes):\n",
        "        fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_pred1[:, i])\n",
        "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "    for i in range(n_classes, 2*n_classes):\n",
        "        fpr[i], tpr[i], _ = roc_curve(y_true[:, i-n_classes], y_pred2[:, i-n_classes])\n",
        "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "    plt.figure(figsize=(12, 8))\n",
        "\n",
        "    if class_names:\n",
        "        for i in range(0, 2*n_classes):\n",
        "          plt.plot(fpr[i], tpr[i], lw=2,\n",
        "                    label=f'{model_names[i]}: {class_names[i%2]} class (AUC = {roc_auc[i]:0.3f})')\n",
        "    else:\n",
        "        for i in range(2*n_classes):\n",
        "            plt.plot(fpr[i], tpr[i], lw=2,\n",
        "                     label=f'{model_names[i]}: Class {i%2} (AUC = {roc_auc[i]:0.3f})')\n",
        "\n",
        "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.05])\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title(title)\n",
        "    plt.legend(loc='best')\n",
        "\n",
        "    if save:\n",
        "        plt.savefig(title + '.png')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "MjMd_sQxjyG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tree_optimization(model, param_grid, X, Y, cv=10, scoring=None):\n",
        "    '''Optimize Decision Tree Classifier using Grid Search.'''\n",
        "\n",
        "    # Initialize and apply GridSearchCV\n",
        "    grid_cv = GridSearchCV(model, param_grid, cv=cv, verbose=1, n_jobs=-1, scoring=scoring)\n",
        "\n",
        "    tic = time()\n",
        "\n",
        "    grid_cv.fit(X, Y)\n",
        "\n",
        "    toc = time()\n",
        "\n",
        "    print(f\"Done in {(toc - tic)/60:0.2f} minutes\")\n",
        "    print(f\"Best estimator: {grid_cv.best_estimator_} with score {grid_cv.best_score_}\")\n",
        "\n",
        "    return grid_cv.best_estimator_\n",
        "\n",
        "def plot_validation_curve(estimator, X, y, param_name, param_range = np.arange(1, 30), ylim=(0.0, 1.1), cv=10, scoring='accuracy', save=False):\n",
        "    '''Generate a plot of the validation curve for a given hyperparameter.'''\n",
        "\n",
        "    # Calculate scores\n",
        "    train_scores, test_scores = validation_curve(\n",
        "                                estimator, X, y, param_name=param_name, cv=cv, \n",
        "                                param_range=param_range,\n",
        "                                scoring=scoring)\n",
        "\n",
        "    train_scores_mean = np.mean(train_scores, axis = 1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis = 1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "    # Plotting options\n",
        "    plt.figure(figsize=(18, 9))\n",
        "    plt.xlabel(param_name, fontsize=15)\n",
        "    plt.xticks(param_range, fontsize=13)\n",
        "    plt.yticks(np.arange(0, 1.1, 0.1), fontsize=13)\n",
        "    plt.ylabel(\"Hamming Score\", fontsize=15)\n",
        "    plt.ylim(*ylim)\n",
        "\n",
        "    plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"r\")\n",
        "    plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
        "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
        "                 color=\"darkorange\", lw=2)\n",
        "    plt.plot(param_range, test_scores_mean, label=\"Test score\", color=\"g\")\n",
        "    plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
        "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
        "                 color=\"navy\", lw=2)\n",
        "    plt.legend(loc=\"best\", fontsize=15)\n",
        "\n",
        "    if save == True:\n",
        "        plt.savefig('Decision Tree Validation Curve (max_depth).png')\n",
        "\n",
        "    return plt"
      ],
      "metadata": {
        "id": "S1oPl158jzz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def friedman_test(dataset, alpha=0.05, long=False, effect_size=True):\n",
        "    '''Perform the Friedman test on a provided dataset.'''\n",
        "\n",
        "    # Transform dataframe to the appropriate format\n",
        "    if not long:\n",
        "        dataset = dataset.melt(ignore_index=False).reset_index(drop=False)\n",
        "        dataset = dataset.rename(columns={'index': 'Fold', 'variable': 'Feature set', 'value': 'Accuracy'})\n",
        "\n",
        "    # Perform the Friedman test\n",
        "    result = pg.friedman(dataset, dv='Accuracy', within='Fold', subject='Feature set')\n",
        "\n",
        "    fried, p_value = result['Q'][0], result['p-unc'][0]\n",
        "\n",
        "    print(f'Friedman statistic: {fried:0.2f}')\n",
        "    print(f'p-value: {p_value:0.6f}')\n",
        "\n",
        "    if p_value < alpha:\n",
        "        print('\\nThe differences between some of the groups are statistically significant.')\n",
        "        print('Use a post hoc test to determine which classifiers actually differ.')\n",
        "    else:\n",
        "        print('\\nThe differences between the groups are not statistically significant.')\n",
        "\n",
        "    # Calculate effect size\n",
        "    if effect_size == True:\n",
        "        print('\\nRelevant effect size')\n",
        "        print('----------------------')\n",
        "        print(f\"Kendall's W coefficient: {result['W'][0]:0.3f}\")\n",
        "\n",
        "    return result\n",
        "\n",
        "def nemeyi_test(dataset, alpha=0.05, significant_only=False):\n",
        "    '''Perform the Nemeyi Post Hoc test on a provided dataset.'''\n",
        "\n",
        "    # Perform the Nemenyi test\n",
        "    ds_nemeyi = sp.posthoc_nemenyi_friedman(dataset)\n",
        "\n",
        "    if significant_only == True:\n",
        "        print(f'Returning only statistically significant differences (p value < {alpha})')\n",
        "\n",
        "        return ds_nemeyi[ds_nemeyi < alpha]\n",
        "\n",
        "    return ds_nemeyi\n",
        "\n",
        "def wilcoxon_test(x, y, alpha=0.05, alternative='two-sided', effect_size=True):\n",
        "    '''Perform the Wilcoxon-Signed Rank test on two sets of observations.'''\n",
        "\n",
        "    # Perform the Wilcoxon Test\n",
        "    result = pg.wilcoxon(x, y, tail=alternative)\n",
        "\n",
        "    wilcoxon, p_value = result['W-val'][0], result['p-val'][0]\n",
        "\n",
        "    print(f'Wilcoxon statistic: {wilcoxon:0.2f}')\n",
        "    print(f'p-value: {p_value:0.6f}')\n",
        "\n",
        "    if p_value < alpha:\n",
        "        print('\\nThe differences between measurements are statistically significant.')\n",
        "    else:\n",
        "        print('\\nThe differences between measurements are not statistically significant.')\n",
        "\n",
        "    # Calculate effect size\n",
        "    if effect_size == True:\n",
        "        print('\\nRelevant effect sizes')\n",
        "        print('-----------------------')\n",
        "        print(f\"Rank-biserial correlation: {result['RBC'][0]:0.3f}\")\n",
        "        print(f\"Common language effect size: {result['CLES'][0]:0.3f}\")\n",
        "\n",
        "    return result\n",
        "\n",
        "def mcnemar_test(y_true, y_pred1, y_pred2, visualize=True, save=True, title=\"McNemar's table\", model_names=None, effect_size=True):\n",
        "    '''Calculate McNemar's test to compare two classifiers.'''\n",
        "\n",
        "    # Compute 2x2 contingency table\n",
        "    table = mcnemar_table(y_true, y_pred1, y_pred2)\n",
        "\n",
        "    # Plot 2x2 contingency table\n",
        "    if visualize == True:\n",
        "        if model_names:\n",
        "            col_labels=['Rule-based Classifier Correct', 'Rule-based Classifier Incorrect']\n",
        "            row_labels=['Decision Tree Classifier Correct', 'Decision Tree Classifier Incorrect']\n",
        "        else:\n",
        "            col_labels=['Model 1 Correct', 'Model 1 Incorrect']\n",
        "            row_labels=['Model 2 Correct', 'Model 2 Incorrect']\n",
        "\n",
        "        plt.figure(figsize=(12, 9))\n",
        "\n",
        "        ds_table = pd.DataFrame(table, index=row_labels, columns=col_labels)\n",
        "\n",
        "        heatmap = sns.heatmap(ds_table, annot=True, fmt=\"d\", cbar=False, cmap='Blues')\n",
        "\n",
        "        plt.yticks([0.2, 1.15])\n",
        "\n",
        "        plt.title(title)\n",
        "\n",
        "        if save == True:\n",
        "            plt.savefig(title + '.png')\n",
        "\n",
        "    # Calculate test statistic and corresponding p-value\n",
        "    chi2, p = mcnemar(table)\n",
        "\n",
        "    print(f'Chi-squared statistic: {chi2:0.2f}')\n",
        "    print(f'p-value: {p:0.6f}')\n",
        "\n",
        "    # Calculate effect size\n",
        "    if effect_size == True:\n",
        "\n",
        "        # Calculate odds ratio (OR)\n",
        "        b = table[0, 1]\n",
        "        c = table[1, 0]\n",
        "        OR = max(b/c, c/b)\n",
        "\n",
        "        print('\\nRelevant effect size')\n",
        "        print('----------------------')\n",
        "        print(f'Odds Ratio: {OR:0.2f}')\n",
        "\n",
        "        return pd.DataFrame({'chi2': chi2, 'p-val': p, 'OR': OR}, index=['McNemar'])\n",
        "\n",
        "    return pd.DataFrame({'chi2': chi2, 'p-val': p}, index=['McNemar'])"
      ],
      "metadata": {
        "id": "uyQcMYv_j1UK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RulePredict:\n",
        "\n",
        "\n",
        "    def __init__(self, multilabel=True, GC=None, LC=None):\n",
        "        '''Initialize Rule-based Predictor.'''\n",
        "\n",
        "        self.GC = GC\n",
        "        self.LC = LC\n",
        "        self.multilabel = multilabel\n",
        "\n",
        "    def predict(self, X):\n",
        "        '''Predict GC-LC class for the input X.'''\n",
        "\n",
        "        if self.multilabel == True:\n",
        "            return np.array([self.gc_predict(X), self.lc_predict(X)])\n",
        "\n",
        "        elif self.GC == True:\n",
        "            return self.gc_predict(X)\n",
        "\n",
        "        elif self.LC == True:\n",
        "            return self.lc_predict(X)\n",
        "\n",
        "        else:\n",
        "            raise ValueError('Invalid input. Choose a valid method (multilabel, GC, LC).')\n",
        "\n",
        "    def gc_predict_proba(self, X):\n",
        "        '''Predict GC class probabilities of the input samples X.'''\n",
        "\n",
        "        prob = 0\n",
        "\n",
        "        if 100 <= X['BoilingPoint'] <= 350:\n",
        "            prob += 1/3\n",
        "        if 2 <= X['logP']:\n",
        "            prob += 1/3\n",
        "        if X['MW'] <= 700:\n",
        "            prob += 1/3\n",
        "\n",
        "        return prob\n",
        "\n",
        "    def gc_predict(self, X, threshold=0.5):\n",
        "        '''Predict GC class value for X.'''\n",
        "\n",
        "\n",
        "        prob = self.gc_predict_proba(X)\n",
        "\n",
        "        return 1 if prob >= threshold else 0\n",
        "\n",
        "    def lc_predict_proba(self, X):\n",
        "        '''Predict LC class probabilities of the input samples X.'''\n",
        "\n",
        "        prob = 0\n",
        "\n",
        "        if X['logP'] <= 5.91:\n",
        "            prob += 1\n",
        "\n",
        "        return prob\n",
        "\n",
        "    def lc_predict(self, X, threshold=0.5):\n",
        "        '''Predict LC class value for X.'''\n",
        "\n",
        "        prob = self.lc_predict_proba(X)\n",
        "\n",
        "        return 1 if prob >= threshold else 0"
      ],
      "metadata": {
        "id": "yOFoDQA_j29w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dict = \\\n",
        "    {\n",
        "    # experiments to build pipeline\n",
        "    # Note: keys are of the form model_*, which are used to execute the associated values of 'pl' keys\n",
        "    'model_1': {\n",
        "        'name': 'Dummy',\n",
        "        'pl': Pipeline([ ('dummy_clf', DummyClassifier(strategy='stratified', random_state=42)) ])\n",
        "        },\n",
        "    # systematic check of default classifiers + scaling\n",
        "    'model_2': {\n",
        "        'name': 'KNN',\n",
        "        'pl': Pipeline([ ('knn', KNeighborsClassifier(n_jobs=-1)) ])\n",
        "        },\n",
        "    'model_3': {\n",
        "        'name': 'Scaled KNN',\n",
        "        'pl': Pipeline([ ('scaling', StandardScaler()),\n",
        "                        ('knn', KNeighborsClassifier(n_jobs=-1)) ])\n",
        "        },\n",
        "    'model_4': {\n",
        "        'name': 'DT',\n",
        "        'pl': Pipeline([ ('decision-tree', DecisionTreeClassifier(random_state=42)) ])\n",
        "        },\n",
        "    'model_5': {\n",
        "        'name': 'Scaled DT',\n",
        "        'pl': Pipeline([ ('scaling', StandardScaler()),\n",
        "                        ('decision-tree', DecisionTreeClassifier(random_state=42)) ])\n",
        "        },\n",
        "    'model_6': {\n",
        "        'name': 'RF',\n",
        "        'pl': Pipeline([ ('random-forest', RandomForestClassifier(random_state=42, n_estimators=100)) ])\n",
        "        },\n",
        "    'model_7': {\n",
        "        'name': 'Scaled RF',\n",
        "        'pl': Pipeline([ ('scaling', StandardScaler()),\n",
        "                        ('random-forest', RandomForestClassifier(random_state=42, n_estimators=100)) ])\n",
        "        }\n",
        "    }"
      ],
      "metadata": {
        "id": "IzGN5PEDj8hG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load dataset"
      ],
      "metadata": {
        "id": "dj4t-zJtj-Zy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = 'http://users.uoa.gr/~nalygizakis/Applied%20Data%20Science/GC_LC%20dataset.xlsx'\n",
        "ds = pd.read_excel(path)"
      ],
      "metadata": {
        "id": "ALYNwPf7jGlj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "hchOnSTIjKEs",
        "outputId": "7649b95f-6988-4890-c10e-51ff7769f7ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <div id=\"df-9ef62680-a8f2-4339-b660-cb9e0399d82e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>PubChemCID</th>\n",
              "      <th>ChemSpiderID</th>\n",
              "      <th>DTXSID</th>\n",
              "      <th>List</th>\n",
              "      <th>Row</th>\n",
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              "      <th>SRW8</th>\n",
              "      <th>SRW9</th>\n",
              "      <th>SRW10</th>\n",
              "      <th>TSRW</th>\n",
              "      <th>MW</th>\n",
              "      <th>AMW</th>\n",
              "      <th>WTPT-1</th>\n",
              "      <th>WTPT-2</th>\n",
              "      <th>WTPT-3</th>\n",
              "      <th>WTPT-4</th>\n",
              "      <th>WTPT-5</th>\n",
              "      <th>WPATH</th>\n",
              "      <th>WPOL</th>\n",
              "      <th>XLogP</th>\n",
              "      <th>Zagreb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Metformin</td>\n",
              "      <td>CN(C)C(=N)NC(N)=N</td>\n",
              "      <td>4091</td>\n",
              "      <td>3949</td>\n",
              "      <td>DTXSID2023270</td>\n",
              "      <td>GC S70;LC S3</td>\n",
              "      <td>3936;1</td>\n",
              "      <td>GC;LC</td>\n",
              "      <td>AUTOGEN_1_1</td>\n",
              "      <td>268.97</td>\n",
              "      <td>-1.03416</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.2231</td>\n",
              "      <td>0.049774</td>\n",
              "      <td>31.2928</td>\n",
              "      <td>19.874723</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1569.213433</td>\n",
              "      <td>1489.142424</td>\n",
              "      <td>1918.702090</td>\n",
              "      <td>1660.618344</td>\n",
              "      <td>1354.838774</td>\n",
              "      <td>870.740052</td>\n",
              "      <td>245.119392</td>\n",
              "      <td>18.289152</td>\n",
              "      <td>...</td>\n",
              "      <td>4.650545e-02</td>\n",
              "      <td>5.167272e-03</td>\n",
              "      <td>-2.761367</td>\n",
              "      <td>3.518077e+01</td>\n",
              "      <td>3.908975</td>\n",
              "      <td>3.204450</td>\n",
              "      <td>88.99</td>\n",
              "      <td>127.451281</td>\n",
              "      <td>4.169925</td>\n",
              "      <td>3.610918</td>\n",
              "      <td>4.290459</td>\n",
              "      <td>5.068904</td>\n",
              "      <td>5.783825</td>\n",
              "      <td>6.555357</td>\n",
              "      <td>7.284135</td>\n",
              "      <td>8.049746</td>\n",
              "      <td>8.785234</td>\n",
              "      <td>9.546884</td>\n",
              "      <td>75.975462</td>\n",
              "      <td>2.833213</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.043051</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.398163</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.816736</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.267962</td>\n",
              "      <td>8.538955</td>\n",
              "      <td>129.101445</td>\n",
              "      <td>6.455072</td>\n",
              "      <td>16.327527</td>\n",
              "      <td>1.814170</td>\n",
              "      <td>12.922711</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.922711</td>\n",
              "      <td>96</td>\n",
              "      <td>8</td>\n",
              "      <td>0.558</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Caffeine</td>\n",
              "      <td>CN1C(=O)N(C)c2ncn(C)c2C1(=O)</td>\n",
              "      <td>2519</td>\n",
              "      <td>2424</td>\n",
              "      <td>DTXSID0020232</td>\n",
              "      <td>GC S3;LC S3</td>\n",
              "      <td>56;2</td>\n",
              "      <td>GC;LC</td>\n",
              "      <td>AUTOGEN_1_2</td>\n",
              "      <td>430.85</td>\n",
              "      <td>-1.02930</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.4311</td>\n",
              "      <td>0.185847</td>\n",
              "      <td>34.8722</td>\n",
              "      <td>26.751930</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2460.993806</td>\n",
              "      <td>2644.545947</td>\n",
              "      <td>4045.596023</td>\n",
              "      <td>4595.614281</td>\n",
              "      <td>3920.830055</td>\n",
              "      <td>1807.596724</td>\n",
              "      <td>541.256565</td>\n",
              "      <td>69.718320</td>\n",
              "      <td>...</td>\n",
              "      <td>9.793972e-03</td>\n",
              "      <td>6.995694e-04</td>\n",
              "      <td>-6.476383</td>\n",
              "      <td>1.141528e+02</td>\n",
              "      <td>8.153771</td>\n",
              "      <td>6.632553</td>\n",
              "      <td>58.44</td>\n",
              "      <td>162.333078</td>\n",
              "      <td>4.807355</td>\n",
              "      <td>4.343805</td>\n",
              "      <td>5.241747</td>\n",
              "      <td>6.159095</td>\n",
              "      <td>7.072422</td>\n",
              "      <td>7.991931</td>\n",
              "      <td>8.908018</td>\n",
              "      <td>9.827524</td>\n",
              "      <td>10.744473</td>\n",
              "      <td>11.663713</td>\n",
              "      <td>100.952729</td>\n",
              "      <td>3.433987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.812184</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>6.398595</td>\n",
              "      <td>4.727388</td>\n",
              "      <td>8.084254</td>\n",
              "      <td>6.842683</td>\n",
              "      <td>9.824498</td>\n",
              "      <td>10.066754</td>\n",
              "      <td>194.080376</td>\n",
              "      <td>8.086682</td>\n",
              "      <td>27.800102</td>\n",
              "      <td>1.985722</td>\n",
              "      <td>17.761592</td>\n",
              "      <td>5.042091</td>\n",
              "      <td>12.719500</td>\n",
              "      <td>258</td>\n",
              "      <td>25</td>\n",
              "      <td>-0.625</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tramadol</td>\n",
              "      <td>COC1=CC=CC(=C1)[C@@]1(O)CCCC[C@@H]1CN(C)C</td>\n",
              "      <td>33741</td>\n",
              "      <td>31105</td>\n",
              "      <td>DTXSID90858931</td>\n",
              "      <td>GC S70;LC S3</td>\n",
              "      <td>4883;3</td>\n",
              "      <td>GC;LC</td>\n",
              "      <td>AUTOGEN_1_3</td>\n",
              "      <td>355.82</td>\n",
              "      <td>2.63460</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.9385</td>\n",
              "      <td>0.880782</td>\n",
              "      <td>48.7166</td>\n",
              "      <td>47.533825</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3041.759587</td>\n",
              "      <td>3407.600996</td>\n",
              "      <td>4841.639644</td>\n",
              "      <td>5538.531978</td>\n",
              "      <td>5362.040452</td>\n",
              "      <td>5209.336419</td>\n",
              "      <td>4002.672643</td>\n",
              "      <td>2550.984419</td>\n",
              "      <td>...</td>\n",
              "      <td>3.410667e-02</td>\n",
              "      <td>1.795088e-03</td>\n",
              "      <td>-6.418698</td>\n",
              "      <td>1.525807e+02</td>\n",
              "      <td>8.030562</td>\n",
              "      <td>9.552618</td>\n",
              "      <td>32.70</td>\n",
              "      <td>270.347133</td>\n",
              "      <td>5.247928</td>\n",
              "      <td>4.574711</td>\n",
              "      <td>5.416100</td>\n",
              "      <td>6.304449</td>\n",
              "      <td>7.180070</td>\n",
              "      <td>8.076205</td>\n",
              "      <td>8.965079</td>\n",
              "      <td>9.863967</td>\n",
              "      <td>10.758860</td>\n",
              "      <td>11.658886</td>\n",
              "      <td>111.798326</td>\n",
              "      <td>3.713572</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.030438</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.569481</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.215277</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.918425</td>\n",
              "      <td>10.123426</td>\n",
              "      <td>263.188529</td>\n",
              "      <td>5.981557</td>\n",
              "      <td>37.952373</td>\n",
              "      <td>1.997493</td>\n",
              "      <td>8.380485</td>\n",
              "      <td>5.376169</td>\n",
              "      <td>3.004316</td>\n",
              "      <td>670</td>\n",
              "      <td>30</td>\n",
              "      <td>2.881</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Venlafaxine</td>\n",
              "      <td>CN(C)CC(C1=CC=C(C=C1)OC)C2(CCCCC2)O</td>\n",
              "      <td>5656</td>\n",
              "      <td>5454</td>\n",
              "      <td>DTXSID6023737</td>\n",
              "      <td>LC S3</td>\n",
              "      <td>4</td>\n",
              "      <td>LC</td>\n",
              "      <td>AUTOGEN_1_4</td>\n",
              "      <td>363.80</td>\n",
              "      <td>3.03560</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.2265</td>\n",
              "      <td>1.504302</td>\n",
              "      <td>51.6282</td>\n",
              "      <td>50.627411</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>47</td>\n",
              "      <td>20</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3188.055836</td>\n",
              "      <td>3576.079293</td>\n",
              "      <td>5035.348181</td>\n",
              "      <td>5585.453662</td>\n",
              "      <td>5505.661445</td>\n",
              "      <td>5587.717290</td>\n",
              "      <td>4535.861090</td>\n",
              "      <td>3195.301918</td>\n",
              "      <td>...</td>\n",
              "      <td>1.736753e-01</td>\n",
              "      <td>8.683764e-03</td>\n",
              "      <td>-3.501136</td>\n",
              "      <td>1.461606e+02</td>\n",
              "      <td>7.308032</td>\n",
              "      <td>9.969413</td>\n",
              "      <td>32.70</td>\n",
              "      <td>287.643117</td>\n",
              "      <td>5.321928</td>\n",
              "      <td>4.615121</td>\n",
              "      <td>5.442418</td>\n",
              "      <td>6.322565</td>\n",
              "      <td>7.181592</td>\n",
              "      <td>8.069342</td>\n",
              "      <td>8.941545</td>\n",
              "      <td>9.831830</td>\n",
              "      <td>10.710120</td>\n",
              "      <td>11.601275</td>\n",
              "      <td>113.715808</td>\n",
              "      <td>3.761200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.068904</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.588926</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.206038</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.875242</td>\n",
              "      <td>10.087932</td>\n",
              "      <td>277.204179</td>\n",
              "      <td>5.897961</td>\n",
              "      <td>39.924107</td>\n",
              "      <td>1.996205</td>\n",
              "      <td>8.362280</td>\n",
              "      <td>5.349601</td>\n",
              "      <td>3.012679</td>\n",
              "      <td>792</td>\n",
              "      <td>30</td>\n",
              "      <td>3.239</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>carbamazepine</td>\n",
              "      <td>c1ccc2c(c1)C=Cc3ccccc3N2C(=O)N</td>\n",
              "      <td>2554</td>\n",
              "      <td>2457</td>\n",
              "      <td>DTXSID4022731</td>\n",
              "      <td>GC S3;LC S3</td>\n",
              "      <td>115;5</td>\n",
              "      <td>GC;LC</td>\n",
              "      <td>AUTOGEN_1_5</td>\n",
              "      <td>410.02</td>\n",
              "      <td>3.38720</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.1599</td>\n",
              "      <td>0.025568</td>\n",
              "      <td>20.2612</td>\n",
              "      <td>37.403516</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2824.514682</td>\n",
              "      <td>3178.387104</td>\n",
              "      <td>4614.262884</td>\n",
              "      <td>5434.720145</td>\n",
              "      <td>5261.654319</td>\n",
              "      <td>4859.088099</td>\n",
              "      <td>2334.946963</td>\n",
              "      <td>730.517259</td>\n",
              "      <td>...</td>\n",
              "      <td>1.942890e-16</td>\n",
              "      <td>1.079383e-17</td>\n",
              "      <td>-65.118933</td>\n",
              "      <td>5.000075e+06</td>\n",
              "      <td>277781.962941</td>\n",
              "      <td>27.764934</td>\n",
              "      <td>46.33</td>\n",
              "      <td>218.818929</td>\n",
              "      <td>5.169925</td>\n",
              "      <td>4.574711</td>\n",
              "      <td>5.442418</td>\n",
              "      <td>6.336826</td>\n",
              "      <td>7.227662</td>\n",
              "      <td>8.128290</td>\n",
              "      <td>9.025335</td>\n",
              "      <td>9.926960</td>\n",
              "      <td>10.825979</td>\n",
              "      <td>11.727642</td>\n",
              "      <td>111.215823</td>\n",
              "      <td>3.713572</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.030438</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.561031</td>\n",
              "      <td>2.708050</td>\n",
              "      <td>8.191186</td>\n",
              "      <td>5.379897</td>\n",
              "      <td>9.872048</td>\n",
              "      <td>10.091418</td>\n",
              "      <td>236.094963</td>\n",
              "      <td>7.869832</td>\n",
              "      <td>37.243138</td>\n",
              "      <td>2.069063</td>\n",
              "      <td>8.382014</td>\n",
              "      <td>2.444637</td>\n",
              "      <td>5.937377</td>\n",
              "      <td>521</td>\n",
              "      <td>33</td>\n",
              "      <td>4.160</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1455 columns</p>\n",
              "</div>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9ef62680-a8f2-4339-b660-cb9e0399d82e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9ef62680-a8f2-4339-b660-cb9e0399d82e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Name                                     SMILES  ...  XLogP Zagreb\n",
              "0      Metformin                          CN(C)C(=N)NC(N)=N  ...  0.558     36\n",
              "1       Caffeine               CN1C(=O)N(C)c2ncn(C)c2C1(=O)  ... -0.625     76\n",
              "2       Tramadol  COC1=CC=CC(=C1)[C@@]1(O)CCCC[C@@H]1CN(C)C  ...  2.881     96\n",
              "3    Venlafaxine        CN(C)CC(C1=CC=C(C=C1)OC)C2(CCCCC2)O  ...  3.239    100\n",
              "4  carbamazepine             c1ccc2c(c1)C=Cc3ccccc3N2C(=O)N  ...  4.160     96\n",
              "\n",
              "[5 rows x 1455 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initial feature set\n",
        "X = ds.iloc[:, 9:]\n",
        "\n",
        "# Target variable (multilabel)\n",
        "mlb = MultiLabelBinarizer()\n",
        "mlb.fit([['GC', 'LC']])\n",
        "y = mlb.transform(ds['Response'].str.split(';'))\n",
        "\n",
        "print(X.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dXDypq_jhF4",
        "outputId": "c373e2cc-fde5-4df3-d9db-f340c30a1cab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(6431, 1446) (6431, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
        "\n",
        "print(f'X_train = {X_train.shape}, Y_train = {Y_train.shape}, X_test = {X_test.shape}, Y_test = {Y_test.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qo6W7aiqkPRX",
        "outputId": "f866b4cc-2f60-4127-8f1d-8b15d5f405cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train = (5144, 1446), Y_train = (5144, 2), X_test = (1287, 1446), Y_test = (1287, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build the model"
      ],
      "metadata": {
        "id": "9v1tcXL1uBH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Decision Tree\n",
        "dtree = DecisionTreeClassifier(max_depth=4, random_state=42)\n",
        "\n",
        "# Transform Train/Test set using selected features\n",
        "sel_feat = ['gmin', 'TopoPSA', 'BoilingPoint', 'BCUTc-1l', 'nN', 'nBase', 'MLFER_A', 'hmax']\n",
        "X_sel = X_train[sel_feat]\n",
        "X_test_sel = X_test[sel_feat]\n",
        "\n",
        "# Train on final dataset\n",
        "dtree.fit(X_sel, Y_train)\n",
        "\n",
        "# Predict on test set\n",
        "tree_pred = dtree.predict(X_test_sel)\n",
        "\n",
        "# Save model\n",
        "dump(dtree, 'DecisionTree.joblib')\n",
        "\n",
        "title = 'Classification Report - Decision Tree Classifier'\n",
        "\n",
        "# Evaluate\n",
        "tree_report = general_classification_report(Y_test, tree_pred, title=title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUWjp2qYjb86",
        "outputId": "733d57a3-1d6f-49b9-e917-7d4f6d3a3d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report - Decision Tree Classifier\n",
            "------------------------------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.88      0.85       977\n",
            "           1       0.76      0.96      0.85       794\n",
            "\n",
            "   micro avg       0.79      0.92      0.85      1771\n",
            "   macro avg       0.79      0.92      0.85      1771\n",
            "weighted avg       0.80      0.92      0.85      1771\n",
            " samples avg       0.82      0.92      0.84      1771\n",
            "\n",
            "\n",
            "Accuracy per class\n",
            "GC = 0.766\n",
            "LC = 0.791\n",
            "\n",
            "Label-based accuracy = 0.779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = multilabel_confusion_matrix(Y_test, tree_pred)\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 7))\n",
        "\n",
        "fig.suptitle('Confusion Matrix - Decision Tree Classifier', y=1.05)\n",
        "\n",
        "show_confusion_matrix(cm[0], ax[0], 'GC', ['N', 'Y'])\n",
        "show_confusion_matrix(cm[1], ax[1], 'LC', ['N', 'Y'])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "IEhGiZBLkXW2",
        "outputId": "e7de26d2-b57f-417b-9af0-1d897271b3eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export tree to DOT data\n",
        "dot_data = export_graphviz(dtree, out_file=None,\n",
        "                           feature_names=X_sel.columns,\n",
        "                           class_names=['GC', 'LC'],\n",
        "                           filled=True, proportion=True)\n",
        "\n",
        "# Draw and save graph\n",
        "graph = graphviz.Source(dot_data, format='PNG')\n",
        "graph.render(\"decision_tree_graph\")\n",
        "\n",
        "graph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "id": "DS6Xkp3SkXNq",
        "outputId": "1d3146d1-464f-40a2-cc7f-3bd3a0ba4197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f4b714c4090>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"2592pt\" height=\"552pt\"\n viewBox=\"0.00 0.00 2592.00 552.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 548)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-548 2588,-548 2588,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<polygon fill=\"#fefbf8\" stroke=\"#000000\" points=\"1351,-544 1187,-544 1187,-461 1351,-461 1351,-544\"/>\n<text text-anchor=\"middle\" x=\"1269\" y=\"-528.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">BoilingPoint &lt;= 293.675</text>\n<text text-anchor=\"middle\" x=\"1269\" y=\"-513.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.419</text>\n<text text-anchor=\"middle\" x=\"1269\" y=\"-498.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 100.0%</text>\n<text text-anchor=\"middle\" x=\"1269\" y=\"-483.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.241, 0.759]</text>\n<text text-anchor=\"middle\" x=\"1269\" y=\"-468.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.383, 0.617]]</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<polygon fill=\"#f5cdb0\" stroke=\"#000000\" points=\"1002,-425 850,-425 850,-342 1002,-342 1002,-425\"/>\n<text text-anchor=\"middle\" x=\"926\" y=\"-409.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nBase &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"926\" y=\"-394.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.26</text>\n<text text-anchor=\"middle\" x=\"926\" y=\"-379.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 27.1%</text>\n<text text-anchor=\"middle\" x=\"926\" y=\"-364.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.085, 0.915]</text>\n<text text-anchor=\"middle\" x=\"926\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.759, 0.241]]</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1186.843,-473.9965C1133.9319,-455.6396 1065.2798,-431.8215 1011.688,-413.2285\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1012.7991,-409.9094 1002.2043,-409.9382 1010.5047,-416.5227 1012.7991,-409.9094\"/>\n<text text-anchor=\"middle\" x=\"1013.1112\" y=\"-428.7336\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 16 -->\n<g id=\"node17\" 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315,-187\"/>\n<text text-anchor=\"middle\" x=\"239\" y=\"-171.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">TopoPSA &lt;= 52.46</text>\n<text text-anchor=\"middle\" x=\"239\" y=\"-156.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.171</text>\n<text text-anchor=\"middle\" x=\"239\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 23.4%</text>\n<text text-anchor=\"middle\" x=\"239\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.037, 0.963]</text>\n<text text-anchor=\"middle\" x=\"239\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.839, 0.161]]</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M409.8764,-227.8251C382.878,-214.8178 352.3192,-200.0951 324.5853,-186.7334\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" 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class=\"edge\">\n<title>17&#45;&gt;18</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1573.2711,-222.8796C1566.9667,-214.0534 1560.249,-204.6485 1553.7476,-195.5466\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1556.5163,-193.4011 1547.8558,-187.2981 1550.8201,-197.4698 1556.5163,-193.4011\"/>\n</g>\n<!-- 21 -->\n<g id=\"node22\" class=\"node\">\n<title>21</title>\n<polygon fill=\"#eeaf81\" stroke=\"#000000\" points=\"1764,-187 1612,-187 1612,-104 1764,-104 1764,-187\"/>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-171.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">hmax &lt;= 0.842</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-156.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.158</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2.3%</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.058, 0.942]</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.883, 0.117]]</text>\n</g>\n<!-- 17&#45;&gt;21 -->\n<g id=\"edge21\" class=\"edge\">\n<title>17&#45;&gt;21</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1632.7289,-222.8796C1639.0333,-214.0534 1645.751,-204.6485 1652.2524,-195.5466\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1655.1799,-197.4698 1658.1442,-187.2981 1649.4837,-193.4011 1655.1799,-197.4698\"/>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<polygon fill=\"#f6d3ba\" stroke=\"#000000\" points=\"1424,-68 1272,-68 1272,0 1424,0 1424,-68\"/>\n<text text-anchor=\"middle\" x=\"1348\" y=\"-52.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.282</text>\n<text text-anchor=\"middle\" x=\"1348\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1.2%</text>\n<text text-anchor=\"middle\" x=\"1348\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.667, 0.333]</text>\n<text text-anchor=\"middle\" x=\"1348\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.063, 0.937]]</text>\n</g>\n<!-- 18&#45;&gt;19 -->\n<g id=\"edge19\" class=\"edge\">\n<title>18&#45;&gt;19</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1454.6983,-103.9815C1439.6258,-94.0957 1423.5778,-83.5701 1408.6284,-73.7651\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1410.2506,-70.6434 1399.9691,-68.0856 1406.4115,-76.4968 1410.2506,-70.6434\"/>\n</g>\n<!-- 20 -->\n<g id=\"node21\" class=\"node\">\n<title>20</title>\n<polygon fill=\"#fbebe0\" stroke=\"#000000\" points=\"1594,-68 1442,-68 1442,0 1594,0 1594,-68\"/>\n<text text-anchor=\"middle\" x=\"1518\" y=\"-52.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.365</text>\n<text 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font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.118</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2.3%</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [[0.034, 0.966]</text>\n<text text-anchor=\"middle\" x=\"1688\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">[0.906, 0.094]]</text>\n</g>\n<!-- 21&#45;&gt;22 -->\n<g id=\"edge22\" class=\"edge\">\n<title>21&#45;&gt;22</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1688,-103.9815C1688,-95.618 1688,-86.7965 1688,-78.3409\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1691.5001,-78.2636 1688,-68.2637 1684.5001,-78.2637 1691.5001,-78.2636\"/>\n</g>\n<!-- 23 -->\n<g id=\"node24\" class=\"node\">\n<title>23</title>\n<polygon fill=\"#e58139\" stroke=\"#000000\" points=\"1904,-68 1782,-68 1782,0 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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "0hBrH8gJoOGz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path2 = 'http://users.uoa.gr/~nalygizakis/Applied%20Data%20Science/DescriptorsSusDat65000.xlsx'\n",
        "for_prediction = pd.read_excel(path2)"
      ],
      "metadata": {
        "id": "m9x07YytkXAL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for_prediction.replace([np.inf, -np.inf], np.nan, inplace=True)"
      ],
      "metadata": {
        "id": "4yrfsgyt1ITa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for_prediction.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1lEzcFWz-4i",
        "outputId": "d88993b7-7bfe-4d40-ef1c-2eaf2bc732d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>TopoPSA</th>\n",
              "      <th>BoilingPoint</th>\n",
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              "      <th>nBase</th>\n",
              "      <th>MLFER_A</th>\n",
              "      <th>hmax</th>\n",
              "      <th>OK</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    </tr>\n",
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              "      <td>0.606</td>\n",
              "      <td>1.348541</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NS00000003</td>\n",
              "      <td>Sulfaguanidine</td>\n",
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              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.783</td>\n",
              "      <td>1.270249</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NS00000004</td>\n",
              "      <td>Sulfamerazine</td>\n",
              "      <td>O=S(=O)(NC1=NC=CC(=N1)C)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.328765</td>\n",
              "      <td>105.29</td>\n",
              "      <td>439.59</td>\n",
              "      <td>-0.302524</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.363025</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NS00000005</td>\n",
              "      <td>Sulfamethizole</td>\n",
              "      <td>O=S(=O)(NC1=NN=C(S1)C)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.175221</td>\n",
              "      <td>130.59</td>\n",
              "      <td>471.67</td>\n",
              "      <td>-0.302510</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.309658</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65686</th>\n",
              "      <td>NS00066792</td>\n",
              "      <td>2-methylbutyl hexanoate</td>\n",
              "      <td>O=C(OCC(C)CC)CCCCC</td>\n",
              "      <td>-0.033565</td>\n",
              "      <td>26.30</td>\n",
              "      <td>218.34</td>\n",
              "      <td>-0.315388</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.555903</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65687</th>\n",
              "      <td>NS00066793</td>\n",
              "      <td>(1-methyl-1,2-ethanediyl)bis[oxy(methyl-2,1-et...</td>\n",
              "      <td>O=C(OCC(OCC(OCC(OC(=O)C=C)C)C)C)C=C</td>\n",
              "      <td>-0.479067</td>\n",
              "      <td>71.06</td>\n",
              "      <td>309.97</td>\n",
              "      <td>-0.373722</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.749395</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65688</th>\n",
              "      <td>NS00066794</td>\n",
              "      <td>2-bromobutyryl bromide</td>\n",
              "      <td>O=C(Br)C(Br)CC</td>\n",
              "      <td>0.160445</td>\n",
              "      <td>17.07</td>\n",
              "      <td>202.44</td>\n",
              "      <td>-0.276125</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.507935</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65689</th>\n",
              "      <td>NS00066795</td>\n",
              "      <td>Oct-2-en-4-one</td>\n",
              "      <td>O=C(C=CC)CCCC</td>\n",
              "      <td>0.249398</td>\n",
              "      <td>17.07</td>\n",
              "      <td>170.18</td>\n",
              "      <td>-0.294387</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.474653</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65690</th>\n",
              "      <td>NS00066796</td>\n",
              "      <td>2,5-dimethylthiophen-3-yl methyl ketone</td>\n",
              "      <td>O=C(C=1C=C(SC1C)C)C</td>\n",
              "      <td>-0.796703</td>\n",
              "      <td>42.37</td>\n",
              "      <td>228.05</td>\n",
              "      <td>-0.294001</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.464491</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65099 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c02b071-d9ef-415a-9902-38ef8e967cee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c02b071-d9ef-415a-9902-38ef8e967cee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c02b071-d9ef-415a-9902-38ef8e967cee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Mol_Name  ...    OK\n",
              "0      NS00000001  ...  Okay\n",
              "1      NS00000002  ...  Okay\n",
              "2      NS00000003  ...  Okay\n",
              "3      NS00000004  ...  Okay\n",
              "4      NS00000005  ...  Okay\n",
              "...           ...  ...   ...\n",
              "65686  NS00066792  ...  Okay\n",
              "65687  NS00066793  ...  Okay\n",
              "65688  NS00066794  ...  Okay\n",
              "65689  NS00066795  ...  Okay\n",
              "65690  NS00066796  ...  Okay\n",
              "\n",
              "[65099 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for_prediction = for_prediction[for_prediction['BCUTc-1l'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['gmin'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['TopoPSA'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['BoilingPoint'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['nN'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['nBase'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['MLFER_A'].notna()]\n",
        "for_prediction = for_prediction[for_prediction['hmax'].notna()]"
      ],
      "metadata": {
        "id": "uQ3iuvxK5Whu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RESZNdW6AQG",
        "outputId": "62a0f468-b304-4945-ae93-c7a09c1a4a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a3672b7f-2877-4b07-9097-a9425e47752f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mol_Name</th>\n",
              "      <th>Name</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>gmin</th>\n",
              "      <th>TopoPSA</th>\n",
              "      <th>BoilingPoint</th>\n",
              "      <th>BCUTc-1l</th>\n",
              "      <th>nN</th>\n",
              "      <th>nBase</th>\n",
              "      <th>MLFER_A</th>\n",
              "      <th>hmax</th>\n",
              "      <th>OK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NS00000001</td>\n",
              "      <td>Sulfaclozine</td>\n",
              "      <td>O=S(=O)(NC1=NC(Cl)=CN=C1)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.288090</td>\n",
              "      <td>105.29</td>\n",
              "      <td>446.69</td>\n",
              "      <td>-0.302532</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.351089</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NS00000002</td>\n",
              "      <td>Sulfachloropyridazine</td>\n",
              "      <td>O=S(=O)(NC1=NN=C(Cl)C=C1)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.284635</td>\n",
              "      <td>105.29</td>\n",
              "      <td>485.02</td>\n",
              "      <td>-0.302514</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.348541</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NS00000003</td>\n",
              "      <td>Sulfaguanidine</td>\n",
              "      <td>O=S(=O)(NC(=N)N)C1=CC=C(N)C=C1</td>\n",
              "      <td>-5.302418</td>\n",
              "      <td>130.44</td>\n",
              "      <td>410.20</td>\n",
              "      <td>-0.302561</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.783</td>\n",
              "      <td>1.270249</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NS00000004</td>\n",
              "      <td>Sulfamerazine</td>\n",
              "      <td>O=S(=O)(NC1=NC=CC(=N1)C)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.328765</td>\n",
              "      <td>105.29</td>\n",
              "      <td>439.59</td>\n",
              "      <td>-0.302524</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.363025</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NS00000005</td>\n",
              "      <td>Sulfamethizole</td>\n",
              "      <td>O=S(=O)(NC1=NN=C(S1)C)C2=CC=C(N)C=C2</td>\n",
              "      <td>-5.175221</td>\n",
              "      <td>130.59</td>\n",
              "      <td>471.67</td>\n",
              "      <td>-0.302510</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.309658</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65686</th>\n",
              "      <td>NS00066792</td>\n",
              "      <td>2-methylbutyl hexanoate</td>\n",
              "      <td>O=C(OCC(C)CC)CCCCC</td>\n",
              "      <td>-0.033565</td>\n",
              "      <td>26.30</td>\n",
              "      <td>218.34</td>\n",
              "      <td>-0.315388</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.555903</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65687</th>\n",
              "      <td>NS00066793</td>\n",
              "      <td>(1-methyl-1,2-ethanediyl)bis[oxy(methyl-2,1-et...</td>\n",
              "      <td>O=C(OCC(OCC(OCC(OC(=O)C=C)C)C)C)C=C</td>\n",
              "      <td>-0.479067</td>\n",
              "      <td>71.06</td>\n",
              "      <td>309.97</td>\n",
              "      <td>-0.373722</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.749395</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65688</th>\n",
              "      <td>NS00066794</td>\n",
              "      <td>2-bromobutyryl bromide</td>\n",
              "      <td>O=C(Br)C(Br)CC</td>\n",
              "      <td>0.160445</td>\n",
              "      <td>17.07</td>\n",
              "      <td>202.44</td>\n",
              "      <td>-0.276125</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.507935</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65689</th>\n",
              "      <td>NS00066795</td>\n",
              "      <td>Oct-2-en-4-one</td>\n",
              "      <td>O=C(C=CC)CCCC</td>\n",
              "      <td>0.249398</td>\n",
              "      <td>17.07</td>\n",
              "      <td>170.18</td>\n",
              "      <td>-0.294387</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.474653</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65690</th>\n",
              "      <td>NS00066796</td>\n",
              "      <td>2,5-dimethylthiophen-3-yl methyl ketone</td>\n",
              "      <td>O=C(C=1C=C(SC1C)C)C</td>\n",
              "      <td>-0.796703</td>\n",
              "      <td>42.37</td>\n",
              "      <td>228.05</td>\n",
              "      <td>-0.294001</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.464491</td>\n",
              "      <td>Okay</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65100 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3672b7f-2877-4b07-9097-a9425e47752f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a3672b7f-2877-4b07-9097-a9425e47752f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a3672b7f-2877-4b07-9097-a9425e47752f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Mol_Name  ...    OK\n",
              "0      NS00000001  ...  Okay\n",
              "1      NS00000002  ...  Okay\n",
              "2      NS00000003  ...  Okay\n",
              "3      NS00000004  ...  Okay\n",
              "4      NS00000005  ...  Okay\n",
              "...           ...  ...   ...\n",
              "65686  NS00066792  ...  Okay\n",
              "65687  NS00066793  ...  Okay\n",
              "65688  NS00066794  ...  Okay\n",
              "65689  NS00066795  ...  Okay\n",
              "65690  NS00066796  ...  Okay\n",
              "\n",
              "[65100 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NORMAN_ID=for_prediction['Mol_Name']\n",
        "Name=for_prediction['Name']\n",
        "SMILES=for_prediction['SMILES']"
      ],
      "metadata": {
        "id": "bShI-T5P0UP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sel_feat = ['gmin', 'TopoPSA', 'BoilingPoint', 'BCUTc-1l', 'nN', 'nBase', 'MLFER_A', 'hmax']\n",
        "for_prediction = for_prediction[sel_feat]\n",
        "\n",
        "for_prediction.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejbypaCVn-wr",
        "outputId": "04e669e7-3872-4623-956f-e2e0861d0f89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-744b3467-1b0a-457b-aaa4-eff77bd92ac9\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gmin</th>\n",
              "      <th>TopoPSA</th>\n",
              "      <th>BoilingPoint</th>\n",
              "      <th>BCUTc-1l</th>\n",
              "      <th>nN</th>\n",
              "      <th>nBase</th>\n",
              "      <th>MLFER_A</th>\n",
              "      <th>hmax</th>\n",
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              "      <th>0</th>\n",
              "      <td>-5.288090</td>\n",
              "      <td>105.29</td>\n",
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              "      <td>-0.302532</td>\n",
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              "      <td>1.351089</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-5.284635</td>\n",
              "      <td>105.29</td>\n",
              "      <td>485.02</td>\n",
              "      <td>-0.302514</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.348541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-5.302418</td>\n",
              "      <td>130.44</td>\n",
              "      <td>410.20</td>\n",
              "      <td>-0.302561</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.783</td>\n",
              "      <td>1.270249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-5.328765</td>\n",
              "      <td>105.29</td>\n",
              "      <td>439.59</td>\n",
              "      <td>-0.302524</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.363025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-5.175221</td>\n",
              "      <td>130.59</td>\n",
              "      <td>471.67</td>\n",
              "      <td>-0.302510</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.309658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-744b3467-1b0a-457b-aaa4-eff77bd92ac9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-744b3467-1b0a-457b-aaa4-eff77bd92ac9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-744b3467-1b0a-457b-aaa4-eff77bd92ac9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       gmin  TopoPSA  BoilingPoint  BCUTc-1l  nN  nBase  MLFER_A      hmax\n",
              "0 -5.288090   105.29        446.69 -0.302532   4    0.0    0.606  1.351089\n",
              "1 -5.284635   105.29        485.02 -0.302514   4    0.0    0.606  1.348541\n",
              "2 -5.302418   130.44        410.20 -0.302561   4    3.0    0.783  1.270249\n",
              "3 -5.328765   105.29        439.59 -0.302524   4    0.0    0.606  1.363025\n",
              "4 -5.175221   130.59        471.67 -0.302510   4    0.0    0.606  1.309658"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05c5jpun4uYM",
        "outputId": "c3358050-e3a0-41c7-8849-da7aef2013d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-24c99960-8ddf-471d-b99a-53f8ca89ff42\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gmin</th>\n",
              "      <th>TopoPSA</th>\n",
              "      <th>BoilingPoint</th>\n",
              "      <th>BCUTc-1l</th>\n",
              "      <th>nN</th>\n",
              "      <th>nBase</th>\n",
              "      <th>MLFER_A</th>\n",
              "      <th>hmax</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-5.288090</td>\n",
              "      <td>105.29</td>\n",
              "      <td>446.69</td>\n",
              "      <td>-0.302532</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.351089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-5.284635</td>\n",
              "      <td>105.29</td>\n",
              "      <td>485.02</td>\n",
              "      <td>-0.302514</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.348541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-5.302418</td>\n",
              "      <td>130.44</td>\n",
              "      <td>410.20</td>\n",
              "      <td>-0.302561</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.783</td>\n",
              "      <td>1.270249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-5.328765</td>\n",
              "      <td>105.29</td>\n",
              "      <td>439.59</td>\n",
              "      <td>-0.302524</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.363025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-5.175221</td>\n",
              "      <td>130.59</td>\n",
              "      <td>471.67</td>\n",
              "      <td>-0.302510</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606</td>\n",
              "      <td>1.309658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65686</th>\n",
              "      <td>-0.033565</td>\n",
              "      <td>26.30</td>\n",
              "      <td>218.34</td>\n",
              "      <td>-0.315388</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.555903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65687</th>\n",
              "      <td>-0.479067</td>\n",
              "      <td>71.06</td>\n",
              "      <td>309.97</td>\n",
              "      <td>-0.373722</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.749395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65688</th>\n",
              "      <td>0.160445</td>\n",
              "      <td>17.07</td>\n",
              "      <td>202.44</td>\n",
              "      <td>-0.276125</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.507935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65689</th>\n",
              "      <td>0.249398</td>\n",
              "      <td>17.07</td>\n",
              "      <td>170.18</td>\n",
              "      <td>-0.294387</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.474653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65690</th>\n",
              "      <td>-0.796703</td>\n",
              "      <td>42.37</td>\n",
              "      <td>228.05</td>\n",
              "      <td>-0.294001</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.464491</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65100 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24c99960-8ddf-471d-b99a-53f8ca89ff42')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24c99960-8ddf-471d-b99a-53f8ca89ff42 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24c99960-8ddf-471d-b99a-53f8ca89ff42');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           gmin  TopoPSA  BoilingPoint  BCUTc-1l  nN  nBase  MLFER_A      hmax\n",
              "0     -5.288090   105.29        446.69 -0.302532   4    0.0    0.606  1.351089\n",
              "1     -5.284635   105.29        485.02 -0.302514   4    0.0    0.606  1.348541\n",
              "2     -5.302418   130.44        410.20 -0.302561   4    3.0    0.783  1.270249\n",
              "3     -5.328765   105.29        439.59 -0.302524   4    0.0    0.606  1.363025\n",
              "4     -5.175221   130.59        471.67 -0.302510   4    0.0    0.606  1.309658\n",
              "...         ...      ...           ...       ...  ..    ...      ...       ...\n",
              "65686 -0.033565    26.30        218.34 -0.315388   0    0.0    0.003  0.555903\n",
              "65687 -0.479067    71.06        309.97 -0.373722   0    0.0    0.003  0.749395\n",
              "65688  0.160445    17.07        202.44 -0.276125   0    0.0    0.022  0.507935\n",
              "65689  0.249398    17.07        170.18 -0.294387   0    0.0    0.003  0.474653\n",
              "65690 -0.796703    42.37        228.05 -0.294001   0    0.0    0.003  0.464491\n",
              "\n",
              "[65100 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test set\n",
        "results_for_prediction = dtree.predict_proba(for_prediction)\n",
        "results_for_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiylbwO4omhe",
        "outputId": "6d150928-51a7-4355-e6b2-6212b1a332b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.22890257, 0.77109743],\n",
              "        [0.22890257, 0.77109743],\n",
              "        [0.22890257, 0.77109743],\n",
              "        ...,\n",
              "        [0.03185841, 0.96814159],\n",
              "        [0.03185841, 0.96814159],\n",
              "        [0.03185841, 0.96814159]]), array([[0.25280695, 0.74719305],\n",
              "        [0.25280695, 0.74719305],\n",
              "        [0.25280695, 0.74719305],\n",
              "        ...,\n",
              "        [0.86548673, 0.13451327],\n",
              "        [0.86548673, 0.13451327],\n",
              "        [0.86548673, 0.13451327]])]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test set\n",
        "results_for_prediction_b = dtree.predict(for_prediction)\n",
        "results_for_prediction_b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2l1CcB6Bh7U",
        "outputId": "87e3ea2e-eeb5-4f6d-9139-41f713a8d5cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 1],\n",
              "       [1, 1],\n",
              "       [1, 1],\n",
              "       ...,\n",
              "       [1, 0],\n",
              "       [1, 0],\n",
              "       [1, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_file = open(\"test.txt\", \"w\")\n",
        "for row in results_for_prediction:\n",
        "    np.savetxt(a_file, row)"
      ],
      "metadata": {
        "id": "61uz-3dv6ere"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Name.to_csv(\"name.csv\")\n",
        "SMILES.to_csv(\"SMILES.csv\")\n",
        "NORMAN_ID.to_csv(\"NORMAN_ID.csv\")"
      ],
      "metadata": {
        "id": "mU0AYRepOh-R"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}